If we could have LLM agents that could inspect other software applications (including LLM agents) and make strong claims about them, that could open up a bunch of neat possibilities.
There could be assurances that apps won’t share/store information.
There could be assurances that apps won’t be controlled by any actor.
There could be assurances that apps can’t be changed in certain ways (eventually).
I assume that all of this should provide most of the benefits people ascribe to blockchain benefits, but without the costs of being on the blockchain.
Some neat options from this:
Companies could request that LLM agents they trust inspect the code of SaaS providers, before doing business with them. This would be ongoing.
These SaaS providers could in turn have their own LLM agents that verify that these investigator LLM agents are trustworthy (i.e. won’t steal anything).
Any bot on social media should be able to provide assurances of how they generate content. I.E. they should be able to demonstrate that they aren’t secretly trying to promote any certain agenda or anything.
Statistical analysis could come with certain assurances. Like, “this analysis was generated with process X, which is understood to have minimal bias.”
It’s often thought that LLMs make web information more opaque and less trustworthy. But with some cleverness, perhaps it could do just the opposite. LLMs could enable information that’s incredibly transparent and trustworthy (to the degrees that matter.)
Criticisms:
“But as LLMs get more capable, they will also be able to make software systems that hide subtler biases/vulnerabilities”
-> This is partially true, but only goes so far. A whole lot of code can be written simply, if desired. We should be able to have conversations like, “This codebase seems needlessly complex, which is a good indication that it can’t be properly trusted. Therefore, we suggest trust other agents more.”
“But the LLM itself is a major black box”
-> True, but it might be difficult to intentionally bias if an observer has access to the training process. Also, it should be understood that off-the-shelf LLMs are more trustworthy than proprietary ones / ones developed for certain applications.
If we could have LLM agents that could inspect other software applications (including LLM agents) and make strong claims about them, that could open up a bunch of neat possibilities.
There could be assurances that apps won’t share/store information.
There could be assurances that apps won’t be controlled by any actor.
There could be assurances that apps can’t be changed in certain ways (eventually).
I assume that all of this should provide most of the benefits people ascribe to blockchain benefits, but without the costs of being on the blockchain.
Some neat options from this:
Companies could request that LLM agents they trust inspect the code of SaaS providers, before doing business with them. This would be ongoing.
These SaaS providers could in turn have their own LLM agents that verify that these investigator LLM agents are trustworthy (i.e. won’t steal anything).
Any bot on social media should be able to provide assurances of how they generate content. I.E. they should be able to demonstrate that they aren’t secretly trying to promote any certain agenda or anything.
Statistical analysis could come with certain assurances. Like, “this analysis was generated with process X, which is understood to have minimal bias.”
It’s often thought that LLMs make web information more opaque and less trustworthy. But with some cleverness, perhaps it could do just the opposite. LLMs could enable information that’s incredibly transparent and trustworthy (to the degrees that matter.)
Criticisms:
“But as LLMs get more capable, they will also be able to make software systems that hide subtler biases/vulnerabilities”
-> This is partially true, but only goes so far. A whole lot of code can be written simply, if desired. We should be able to have conversations like, “This codebase seems needlessly complex, which is a good indication that it can’t be properly trusted. Therefore, we suggest trust other agents more.”
“But the LLM itself is a major black box”
-> True, but it might be difficult to intentionally bias if an observer has access to the training process. Also, it should be understood that off-the-shelf LLMs are more trustworthy than proprietary ones / ones developed for certain applications.